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Commit Graph

2702 Commits

Author SHA1 Message Date
Alexander Smorkalov 4705320496 Merge pull request #28239 from AdwaithBatchu:optimize-stitching-allocations
optimize: replace push_back with emplace_back in core loops
2025-12-23 15:05:35 +03:00
Abhishek Shinde ea9b183d9b Merge pull request #28259 from falloficarus22:fix/bilateral-filter-oob
Fix the out-of-bounds read in cv::bilateralFilter for 32f images #28259

### Root Cause Analysis

The issue was caused by a discrepancy between the image range used to allocate the color weight look-up table (LUT) and the actual range of pixel values encountered during filtering, especially near the image borders.

- Range Computation: `cv::bilateralFilter` computes the min/max values of the source image and allocates a `LUT (expLUT)` of size `kExpNumBins + 2` based on this range.
- Border Padding: If `cv::BORDER_CONSTANT` is used (defaulting to 0), and 0 is outside the image's original range (e.g., an image with values between 100 and 200), the padded image will contain values (0) that create differences larger than those accounted for in the `LUT`.
- Out-of-Bounds Access: When calculating the color weight, the code computes an index `idx` from the absolute difference. If this difference exceeds the expected range, `idx` can reach or exceed `kExpNumBins + 1`. Since the code performs linear interpolation using `expLUT[idx]` and `expLUT[idx + 1]`, an `idx` of `kExpNumBins + 1` causes an access to `expLUT[kExpNumBins + 2]`, which is out of bounds.

### Fix

I implemented a robust clamping mechanism in both the SIMD (AVX/SSE) and scalar paths of the bilateral filter invoker:

- Signature Update: Updated `bilateralFilterInvoker_32f` to accept `kExpNumBins` (the maximum valid `LUT` index).
- Clamping: Clamped the computed color difference (alpha) to `kExpNumBins` before calculating the `LUT` index. This ensures that any difference exceeding the planned range is safely treated as the maximum difference in the `LUT` (which usually corresponds to a weight of 0), avoiding any out-of-bounds memory access.

### Modified Files

Modified Files:
`modules/imgproc/src/bilateral_filter.simd.hpp`: Updated the invoker class and SIMD/scalar loops to clamp the LUT index.
`modules/imgproc/src/bilateral_filter.dispatch.cpp`: Updated the dispatch call site to pass the correct LUT size.

Closes #28254 

### Pull Request Readiness Checklist

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2025-12-22 20:41:58 +03:00
satyam yadav 15fe69b5af Merge pull request #28250 from satyam102006:fix/stackblur-overflow-28233
imgproc: fix heap-buffer-overflow in stackBlur #28233 #28250

### Summary
Fixes a heap-buffer-overflow in `cv::stackBlur` when the kernel size is larger than the image dimensions 
### Changes
* Added input validation to clamp the kernel size to the image dimensions.
* Added a regression test (`regression_28233`) covering 1x1 and small image cases.

Fixes #28233
2025-12-22 11:22:12 +03:00
Alexander Smorkalov 8cf2e774b7 Merge pull request #28208 from asmorkalov:as/filters_stateless
Stateless HAL for filters and morphology. #28208

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2025-12-20 13:30:09 +03:00
Alexander Smorkalov a3380cc128 Code review fixes. 2025-12-19 14:59:45 +03:00
Dmitry Kurtaev 43074571af Merge pull request #28163 from dkurt:d.kurtaev/convexHull_repeats
Keep convexHull output indices monotone if possible #28163

### Pull Request Readiness Checklist

resolves https://github.com/opencv/opencv/issues/24907 ?
resolves https://github.com/opencv/opencv/issues/4954

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2025-12-19 14:41:21 +03:00
AdwaithBatchu 5f0bd387db optimize: replace push_back with emplace_back in core loops
Replaces wasteful temporary object construction with in-place construction using emplace_back. Covers hot paths in objdetect, imgproc, and stitching modules.
2025-12-19 11:56:20 +05:30
Alexander Smorkalov 721bb7289d Merge pull request #28185 from asmorkalov:as/static_analysys_fix
Fixed issues identified by PVS Studio #28185

Partially fixes https://github.com/opencv/opencv/issues/28167
Paper: https://pvs-studio.com/en/blog/posts/cpp/1321/

Closed items: N2, N4, N5, N6, N7, N8, N10, N11, N13, N14.

To be continued...

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2025-12-18 11:32:19 +03:00
Alexander Smorkalov a858fb7cff Stateless HAL for filters and morphology. 2025-12-18 09:36:54 +03:00
zdenyhraz c5d70a7f22 Merge pull request #28146 from zdenyhraz:iterative-phase-correlation
Iterative Phase Correlation #28146

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2025-12-13 12:45:05 +03:00
Alexander Smorkalov 0e2373557b Merge pull request #28119 from galinabykova:approxPolyDP_fix_distance_segment
fix bug in approxPolyDP: calculate distance to a segment, not to a straight line
2025-12-09 10:59:33 +03:00
Vincent Rabaud 4d7ce375fc Increase minAreaRect accuracy
Just keep doubles all the way and avoid arithmetic with CV_PI/2
2025-12-08 09:44:02 +01:00
Galina Bykova fc7e70ef4a fix bug in approxPolyDP: calculate distance to a segment, not to a line 2025-12-01 21:22:11 +03:30
Ghazi-raad a27a13921b Fix LINE_4/LINE_8 swap in drawContours (issue #26413)
Changed condition to correctly route LINE_8 to Line() instead of Line2().

The condition 'line_type == 1 || line_type == 4' was causing LINE_8 (value 8)
to be processed by Line2() which implements 4-connectivity, and LINE_4 (value 4)
to be processed by Line() which was implementing 8-connectivity behavior. This
resulted in swapped line connectivity.

Changed to 'line_type == 1 || line_type == 8' so LINE_8 goes to Line() with
8-connectivity and LINE_4 goes to Line2() with 4-connectivity, matching the
documented behavior where LINE_4 should produce 4-connected lines and LINE_8
should produce 8-connected lines.

Fixes #26413
2025-11-26 21:41:28 +00:00
Dmitry Kurtaev 621ad482d6 Merge pull request #28051 from dkurt:minAreaRect_angle
Correct minAreaRect angle to be in range [-90, 0) #28051

### Pull Request Readiness Checklist

Box angle range over all imgproc tests is in interval `[-90, -0.0581199]`

resolves https://github.com/opencv/opencv/issues/27667
resolves https://github.com/opencv/opencv/issues/19472
resolves https://github.com/opencv/opencv/issues/24436

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2025-11-25 15:56:59 +03:00
Alexander Smorkalov 49607438bd Merge pull request #28048 from asmorkalov:as/stateless_hal_filtering
Improved HAL status check for filter, sepFilter and morphology operations to support stateless HAL
2025-11-22 12:41:05 +03:00
Dmitry Kurtaev 2cc5b69fd1 Merge pull request #28043 from dkurt:d.kurtaev/convexHull_eps
Handle near-zero convexity in convexHull #28043

### Pull Request Readiness Checklist

resolves https://github.com/opencv/opencv/issues/21482
closes https://github.com/opencv/opencv/issues/14401

Also skip a code that determines orientation inside rotatingCalipers and rely on the order after convexHull

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2025-11-21 10:54:38 +03:00
Alexander Smorkalov 308631197f Improved HAL status check for filter, sepFilter and morphology operations to support stateless HAL. 2025-11-20 08:55:03 +03:00
harunresit f26d7e1bf4 Merge pull request #28014 from harunresit:fix-28002-clahe
Added BitShift option to CLAHE #28014

Briefly, this PR is the fix of https://github.com/opencv/opencv/issues/28002

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2025-11-16 14:45:50 +03:00
penghuiho 4c1f32e716 Fix the issue of obtaining the size of the _src 2025-11-13 13:57:41 +08:00
Yuantao Feng 78adab13e9 Merge pull request #27479 from fengyuentau:4x/imgproc/boundingRect-simd
imgproc: supports CV_SIMD_SCALABLE in pointSetBoundingRect #27479

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2025-11-12 18:44:41 +03:00
Alexander Smorkalov a31a52adef Merge pull request #27992 from asmorkalov:as/HoughLines_bias
Fixed standard HoughLines output shift for rho. #27992

Closes: https://github.com/opencv/opencv/issues/25038
Replaces: https://github.com/opencv/opencv/pull/25043

Merge with https://github.com/opencv/opencv_extra/pull/1288

The original implementation introduces systematic shift (-rho/2) for odd indexes. Integer division just gives proper rounding.

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2025-11-12 11:30:14 +03:00
Samaresh Kumar Singh 603bc58d3b Fix #27968: Eliminate unnecessary type conversions in GrabCut initGMMs
Remove performance bottleneck caused by redundant type conversions in the
initGMMs() function's tight nested loops that iterate over all image pixels.

Changes:
- Changed storage vectors from Vec3f to Vec3b to store pixel data directly
  without intermediate conversion
- Modified kmeans preparation to convert Vec3b data to CV_32FC1 only when
  creating the Mat for clustering (using convertTo instead of per-pixel cast)
- Explicitly convert Vec3b to Vec3d only when calling addSample() method

Performance Impact:
Previously: Vec3b -> Vec3f (in loop) -> Vec3d (implicit in addSample)
Now: Vec3b (in loop) -> Vec3d (explicit, only in addSample call)

This eliminates one unnecessary type conversion per pixel in the tight loop,
reducing computational overhead especially for large images.

Fixes #27968
2025-11-08 13:16:01 -06:00
Pierre Chatelier 9fc556a83e Merge pull request #27366 from chacha21:arrowedLine_clipped
Try to fix distant points to save time when ThickLine() calls FillConvexPoly() #27366

Proposal for #27365

cv::clipLine() is useful, but one should take care of a margin to preserve line caps.

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2025-11-07 10:34:52 +03:00
MaximSmolskiy e45d07e95b Refactor minEnclosingCircle 2025-10-16 22:18:39 +03:00
MaximSmolskiy 1b09d7390f Fix minEnclosingCircle 2025-10-15 01:34:14 +03:00
ashish-sriram 19c41c2936 Merge pull request #27822 from amd:fast_blur_simd
Improved blur #27822

* Perform row and column filter operations in a single pass.
* Temporary storage of intermediate results are avoided.
* Impacts 32F and 64F inputs for ksize <=5.

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2025-10-06 11:00:30 +03:00
SaraKuhnert 79793e169e Merge pull request #27369 from SaraKuhnert:minEnclosingPolygon
imgproc: add minEnclosingConvexPolygon #27369

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2025-09-23 22:14:12 +03:00
Madan mohan Manokar 691b1bdc05 Merge pull request #27795 from amd:fast_gaussian_simd
Improved Gaussian Blur #27795

- Horizontal and vertical kernels of 3N131 and 5N14641 are combined for non-border(inner) regions.
- Temporary storage of intermediate results are avoided by combining the kernels.
- Further refinement of other 3N, 5N to be added later.

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2025-09-22 17:30:28 +03:00
Nadezhda Mizonova 9663b582a2 Merge pull request #27720 from nmizonov:fix_ipp_bilateral_tiling
Set limitation to IPP Bilateral Filter tiles number to avoid too small tiles #27720

### Pull Request Readiness Checklist

This PR fixes the following issue in Bilateral Filter tiling in IPP integration: image ROI can't be closer to the image border than the filter window radius. This issue shows itself during separation of the image to tiles for multithreaded processing. If the tile size small enough, the second tile is closer to the upper image border than the bilateral filter radius, which leads to the incorrect result. To fix this, we need a limitation to the tile size - done in this PR.

_Note: red build status looks like unrelated to the current change_

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2025-09-08 16:11:20 +03:00
Aditya Jha 7a1ec54c43 Merge pull request #27641 from Ma-gi-cian:subdiv2d-rect2f-clean
Subdiv2d rect2f clean #27641

Closes https://github.com/opencv/opencv/issues/27623

Changes:

- Added Subdiv2D(Rect2f) constructor overload
- Added initDelaunay(Rect2f) method overload

- No changes to the previous implementation to keep it backward compatible
- Added tests for init and testing with edge case of extremely small coordinates

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See original pull request at : https://github.com/opencv/opencv/pull/27631
2025-08-15 15:05:27 +03:00
Madan mohan Manokar 2762ffe7cc Merge pull request #27433 from amd:fast_bilateral_simd
imgproc: Bilateral filter performance improvement #27433

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2025-08-12 17:50:44 +03:00
Pierre Chatelier e31ff00104 Merge pull request #27599 from chacha21:drawing_stack_alloc
optimize some drawing with stack allocation #27599

Some drawings can try a stack allocation instead of a std::vector

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2025-08-07 17:19:55 +03:00
Maxim Smolskiy 615ceefd0c Merge pull request #27582 from MaximSmolskiy:take_into_account_overflow_for_connected_components
Take into account overflow for connected components #27582

### Pull Request Readiness Checklist

Fix #27568 

The problem was caused by a label type overflow (`debug_example.npy` contains `92103` labels, that doesn't fit in the `CV_16U` (`unsigned short`) type). If pass `CV_32S` instead of `CV_16U` as `ltype` - everything will be calculated successfully

Added overflow detection to throw exception with a clear error message instead of strange segfault/assertion error

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2025-07-29 13:10:01 +03:00
Alexander Smorkalov ec9b8de3fa Merge pull request #27569 from asmorkalov:as/counters_build_fix
Fixed build issue with some old GCC versions #27569

Fixes build for GCC 4.8.5 at least. Looks like the method default signature differs cross GCC versions or newer version has softer checker.

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2025-07-24 11:23:21 +03:00
ekharkov d19dd94dee Moved IPP remap to HAL 2025-07-08 12:31:40 +02:00
eplankin c48dad1d9d Merge pull request #27324 from eplankin:warp_hal_4x
* Moved IPP impl of warpAffine to HAL
Co-authored-by: victorget <victor.getmanskiy@intel.com>
2025-07-04 12:04:31 +03:00
Martin 43112409ef Merge pull request #26974 from klosteraner:Fix-IntersectConvexConvex
Issue 26972: Proper treatment of float values in intersectConvexConvex #26974

As outlined in https://github.com/opencv/opencv/issues/26972 the function `intersectConvexConvex()` may not work as expected in the corner case, where two polygons intersect at a corner. A concrete example is given that I added as unit test. The unit test would fail without the proposed bug fix. I recommend porting the fix to all versions.

Now concerning the fix: When digging into the implementation I found, that when the line intersections are computed, openCV currently does not apply floating point comparison syntax, but pretends that line end points are exact. Instead I replaced the formulation using the eps that is already used in another component of the function in line.277: `epx=1e-5`. IMO that is solid enough, definitely better than assuming an exact floating point comparison is possible.

As a follow up I would suggest to use a scalable eps, s.t. also cases with high floating point numbers would be less error prone. However that would need to be done in all relevant sub steps, not just the line intersection code. So for me outside the scope of this fix.



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2025-06-28 22:06:42 +03:00
Alexander Smorkalov 5ee8919139 Merge pull request #27441 from KAVYANSHTYAGI:codex/find-feature-to-add-for-library
Add Support for Diamond-Shaped Structuring Element in Morphological Operations
2025-06-27 09:39:44 +03:00
Souriya Trinh ba70d1104f Check for empty vector to avoid throwing an exception in LineSegmentDetectorImpl::drawSegments() function. 2025-06-24 04:58:54 +02:00
Yuantao Feng 3259863924 Merge pull request #27368 from fengyuentau:4x/imgproc/CreateHanningWindow-simd
imgproc: vectorize cv::createHanningWindow #27368

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2025-06-20 12:00:37 +03:00
Alexander Smorkalov 972e135479 Merge pull request #26703 from MaximSmolskiy:fix-matchTemplate-with-mask-crash
Fix matchTemplate with mask crash
2025-06-19 10:15:33 +03:00
Dmitry Kurtaev d750d43aa2 Merge pull request #27432 from dkurt:d.kurtaev/ipp_distTransform
Correct IPP distanceTransform results with single thread #27432

### Pull Request Readiness Checklist

resolves #24082

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2025-06-11 17:37:18 +03:00
Kavyansh Tyagi 452882c007 Add diamond structuring element 2025-06-11 15:54:29 +05:30
s-trinh e258f2595e Merge pull request #26299 from s-trinh:feat/getClosestEllipsePoints_2
Add getClosestEllipsePoints() function to get the closest point on an ellipse #26299

Following https://github.com/opencv/opencv/issues/26078, I was thinking that a function to get for a considered 2d point the corresponding closest point (or maybe directly the distance?) on an ellipse could be useful.
This would allow computing the fitting error with `fitEllipse()` for instance.

Code is based from:
- https://stackoverflow.com/questions/22959698/distance-from-given-point-to-given-ellipse/46007540#46007540
- https://blog.chatfield.io/simple-method-for-distance-to-ellipse/
- https://github.com/0xfaded/ellipse_demo

---

Demo code:

<details>
  <summary>code</summary>
 
```cpp
#include <iostream>
#include <opencv2/opencv.hpp>

namespace
{
void scaleApplyColormap(const cv::Mat &img_float, cv::Mat &img)
{
  cv::Mat img_scale = cv::Mat::zeros(img_float.size(), CV_8UC3);

  double min_val = 0, max_val = 0;
  cv::minMaxLoc(img_float, &min_val, &max_val);
  std::cout << "min_val=" << min_val << " ; max_val=" << max_val << std::endl;

  if (max_val - min_val > 1e-2) {
    float a = 255 / (max_val - min_val);
    float b = -a * min_val;

    cv::convertScaleAbs(img_float, img_scale, a, b);
    cv::applyColorMap(img_scale, img, cv::COLORMAP_TURBO);
  }
  else {
    std::cerr << "max_val - min_val <= 1e-2" << std::endl;
  }
}

cv::Mat drawEllipseDistanceMap(const cv::RotatedRect &ellipse_params)
{
  float bb_rect_w = ellipse_params.center.x + ellipse_params.size.width;
  float bb_rect_h = ellipse_params.center.y + ellipse_params.size.height;

  std::vector<cv::Point2f> points_list;
  points_list.resize(1);
  cv::Mat pointsf;
  cv::Mat closest_pts;
  cv::Mat dist_map = cv::Mat::zeros(bb_rect_h*1.5, bb_rect_w*1.5, CV_32F);
  for (int i = 0; i < dist_map.rows; i++) {
    for (int j = 0; j < dist_map.cols; j++) {
      points_list[0].x = j;
      points_list[0].y = i;
      cv::Mat(points_list).convertTo(pointsf, CV_32F);
      cv::getClosestEllipsePoints(ellipse_params, pointsf, closest_pts);
      dist_map.at<float>(i, j) = std::hypot(closest_pts.at<cv::Point2f>(0).x-j, closest_pts.at<cv::Point2f>(0).y-i);
    }
  }

  cv::Mat dist_map_8u;
  scaleApplyColormap(dist_map, dist_map_8u);
  return dist_map_8u;
}
}

int main()
{
  std::vector<cv::Point2f> points_list;

  // [1434, 308], [1434, 309], [1433, 310], [1427, 310], [1427, 312], [1426, 313], [1422, 313], [1422, 314],
  points_list.push_back(cv::Point2f(1434, 308));
  points_list.push_back(cv::Point2f(1434, 309));
  points_list.push_back(cv::Point2f(1433, 310));
  points_list.push_back(cv::Point2f(1427, 310));
  points_list.push_back(cv::Point2f(1427, 312));
  points_list.push_back(cv::Point2f(1426, 313));
  points_list.push_back(cv::Point2f(1422, 313));
  points_list.push_back(cv::Point2f(1422, 314));

  // [1421, 315], [1415, 315], [1415, 316], [1414, 317], [1408, 317], [1408, 319], [1407, 320], [1403, 320],
  points_list.push_back(cv::Point2f(1421, 315));
  points_list.push_back(cv::Point2f(1415, 315));
  points_list.push_back(cv::Point2f(1415, 316));
  points_list.push_back(cv::Point2f(1414, 317));
  points_list.push_back(cv::Point2f(1408, 317));
  points_list.push_back(cv::Point2f(1408, 319));
  points_list.push_back(cv::Point2f(1407, 320));
  points_list.push_back(cv::Point2f(1403, 320));

  // [1403, 321], [1402, 322], [1396, 322], [1396, 323], [1395, 324], [1389, 324], [1389, 326], [1388, 327],
  points_list.push_back(cv::Point2f(1403, 321));
  points_list.push_back(cv::Point2f(1402, 322));
  points_list.push_back(cv::Point2f(1396, 322));
  points_list.push_back(cv::Point2f(1396, 323));
  points_list.push_back(cv::Point2f(1395, 324));
  points_list.push_back(cv::Point2f(1389, 324));
  points_list.push_back(cv::Point2f(1389, 326));
  points_list.push_back(cv::Point2f(1388, 327));

  // [1382, 327], [1382, 328], [1381, 329], [1376, 329], [1376, 330], [1375, 331], [1369, 331], [1369, 333],
  points_list.push_back(cv::Point2f(1382, 327));
  points_list.push_back(cv::Point2f(1382, 328));
  points_list.push_back(cv::Point2f(1381, 329));
  points_list.push_back(cv::Point2f(1376, 329));
  points_list.push_back(cv::Point2f(1376, 330));
  points_list.push_back(cv::Point2f(1375, 331));
  points_list.push_back(cv::Point2f(1369, 331));
  points_list.push_back(cv::Point2f(1369, 333));

  // [1368, 334], [1362, 334], [1362, 335], [1361, 336], [1359, 336], [1359, 1016], [1365, 1016], [1366, 1017],
  points_list.push_back(cv::Point2f(1368, 334));
  points_list.push_back(cv::Point2f(1362, 334));
  points_list.push_back(cv::Point2f(1362, 335));
  points_list.push_back(cv::Point2f(1361, 336));
  points_list.push_back(cv::Point2f(1359, 336));
  points_list.push_back(cv::Point2f(1359, 1016));
  points_list.push_back(cv::Point2f(1365, 1016));
  points_list.push_back(cv::Point2f(1366, 1017));

  // [1366, 1019], [1430, 1019], [1430, 1017], [1431, 1016], [1440, 1016], [1440, 308]
  points_list.push_back(cv::Point2f(1366, 1019));
  points_list.push_back(cv::Point2f(1430, 1019));
  points_list.push_back(cv::Point2f(1430, 1017));
  points_list.push_back(cv::Point2f(1431, 1016));
  points_list.push_back(cv::Point2f(1440, 1016));
  points_list.push_back(cv::Point2f(1440, 308));

  cv::Mat pointsf;
  cv::Mat(points_list).convertTo(pointsf, CV_32F);

  cv::RotatedRect ellipse_params = cv::fitEllipseAMS(pointsf);
  std::cout << "ellipse_params, center=" << ellipse_params.center << " ; size=" << ellipse_params.size
    << " ; angle=" << ellipse_params.angle << std::endl;

  cv::TickMeter tm;
  tm.start();
  cv::Mat dist_map_8u = drawEllipseDistanceMap(ellipse_params);
  tm.stop();
  std::cout << "Elapsed time: " << tm.getAvgTimeSec() << " sec" << std::endl;

  cv::Point center(ellipse_params.center.x, ellipse_params.center.y);
  cv::Point axis(ellipse_params.size.width/2, ellipse_params.size.height/2);
  std::vector<cv::Point> ellipse_pts_list;
  cv::ellipse2Poly(center, axis, ellipse_params.angle, 0, 360, 1, ellipse_pts_list);
  cv::polylines(dist_map_8u, ellipse_pts_list, false, cv::Scalar(0, 0, 0), 3);

  // Points to be fitted
  cv::Mat closest_pts;
  cv::getClosestEllipsePoints(ellipse_params, pointsf, closest_pts);
  for (int i = 0; i < closest_pts.rows; i++) {
    cv::Point pt;
    pt.x = closest_pts.at<cv::Point2f>(i).x;
    pt.y = closest_pts.at<cv::Point2f>(i).y;
    cv::circle(dist_map_8u, pt, 8, cv::Scalar(0, 0, 255), 2);
  }

  cv::imwrite("dist_map_8u.png", dist_map_8u);

  return EXIT_SUCCESS;
}
```
</details>

![image](https://github.com/user-attachments/assets/3345cc86-ba83-44f9-ac78-74058a33a7dc)

---

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2025-06-03 17:12:16 +03:00
Liane Lin 8a0ea789e7 Merge pull request #27149 from liane-lin:4.x
Fix #25696: Solved the problem in Subdiv2D, empty delaunay triangulation #27149

Detailed description

Expected behaviour:
Given 4 points, where no three points are collinear, the Delaunay Triangulation Algorithm should return 2 triangles.

Actual:
The algorithm returns zero triangles in this particular case.

Fix:
The radius of the circumcircle tends to infinity when the points are closer to form collinear points, so the problem occurs because the super-triangles are not large enough,
which then results in certain edges are not swapped. The proposed solution just increases the super triangle, duplicating the value of constant for example.

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2025-05-30 15:20:01 +03:00
Yuantao Feng c37f54aeed Merge pull request #27343 from fengyuentau:4x/build/fix_more_warnings
build: fix more warnings from recent gcc versions after #27337 #27343

More fixings after https://github.com/opencv/opencv/pull/27337

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2025-05-21 16:12:09 +03:00
Yuantao Feng 166f76d224 Merge pull request #27337 from fengyuentau:4x/build/riscv/fix_warnings
build: fix warnings from recent gcc versions #27337

This PR addresses the following found warnings:
- [x] -Wmaybe-uninitialized
- [x] -Wunused-variable
- [x] -Wsign-compare

Tested building with GCC 14.2 (RISC-V 64).

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2025-05-21 09:28:29 +03:00
Alexander Smorkalov 79a5e5276a Merge pull request #27334 from fengyuentau:4x/imgproc/compareHist_chisqr_simd
imgproc: vectorize mode CHISQR and CHISQR_ALT in compareHist
2025-05-21 07:07:57 +03:00
Yuantao Feng 9b08167769 hal/imgproc: add hal for calcHist and implement in hal:riscv-rvv (#27332)
hal/imgproc: add hal for calcHist and implement in hal/riscv-rvv #27332

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2025-05-21 07:07:22 +03:00